Abstract

A numerically effecient, hybrid Eulerian- Lagrangian methodology has been developed to help better understand the complicated two- phase flowfield encountered in rotorcraft brownout environments. The problem of brownout occurs when rotorcraft operate close to surfaces covered with loose particles such as sand, dust or snow. These particles can get entrained, in large quantities, into the rotor wake leading to a potentially hazardous degradation of the pilots visibility. It is believed that a computationally efficient model of this phenomena, validated against available experimental measurements, can be a used as a valuable tool to reveal the underlying physics of rotorcraft brownout. The present work involved the design, development and validation of a hybrid solver for the purpose of modeling brownout-like environments. The proposed methodology combines the numerical efficiency of a free-vortex method with the relatively high-fidelity of a 3D, time-accurate, Reynolds- averaged, Navier-Stokes (RANS) solver. For dual-phase simulations, this hybrid method can be unidirectionally coupled with a sediment tracking algorithm to study cloud development. In the past, large clusters of CPUs have been the standard approach for large simulations involving the numerical solution of PDEs. In recent years, however, an emerging trend is the use of Graphics Processing Units (GPUs), once used only for graphics rendering, to perform scientific computing. These platforms deliver superior computing power and memory bandwidth compared to traditional CPUs and their prowess continues to grow rapidly with each passing generation. CFD simulations have been ported successfully onto GPU platforms in the past. However, the nature of GPU architecture has restricted the set of algorithms that exhibit significant speedups on these platforms - GPUs are optimized for operations where a massively large number of threads, relative to the problem size, are working in parallel, executing identical instructions on disparate datasets. For this reason, most implementations in the scientific literature involve the use of explicit algorithms for time-stepping, reconstruction, etc. To overcome the difficulty associated with implicit methods, the current work proposes a multi-granular approach to reduce performance penalties typically encountered with such schemes. To explore the use of GPUs for RANS simulations, a 3D, time- accurate, implicit, structured, compressible, viscous, turbulent, finite-volume RANS solver was designed and developed in CUDA-C. During the development phase, various strategies for performance optimization were used to make the implementation better suited to the GPU architecture. Validation and verification of the GPU-based solver was performed for both canonical and realistic bench-mark problems on a variety of GPU platforms. In these test- cases, a performance assessment of the GPU-RANS solver indicated that it…